A heuristic approach on how to optimally schedule jobs using a quantum computer.

Publication can be found here.

NOTE: for a more efficient solution check out the "pyqubo" branch.
(works extremely slowly while using simulation instead of real qpu.)

• About the Project
• Getting started
  • Prerequisites
  • Installation
  • Quick Start
• How to cite our work

Note: Numbers in bars represent jobs

Given a set of jobs and a finite number of machines, how should we schedule our jobs on those machines such that all our jobs are completed at the earliest possible time? This question is the job shop scheduling problem!

• python 3.5 or later
• matplotlib (for results visualisation in charts.py)

pip3 install matplotlib

• D'Wave's libraries for interaction with QPU API https://docs.ocean.dwavesys.com/en/latest/overview/install.html

NOTE: If you are okay with using a simulator instead of a real QPU, jump to part 3.

1. Get free API Key at https://www.dwavesys.com/take-leap
2. Configure a solver at https://docs.ocean.dwavesys.com/en/latest/overview/dwavesys.html#dwavesys
3. Clone the repo

git clone https://github.com/mareksubocz/QuantumJSP

python3 demo.py data/ft06.txt

Kurowski K., Wȩglarz J., Subocz M., Różycki R., Waligóra G. (2020) Hybrid Quantum Annealing Heuristic Method for Solving Job Shop Scheduling Problem. In: Krzhizhanovskaya V. et al. (eds) Computational Science – ICCS 2020. ICCS 2020. Lecture Notes in Computer Science, vol 12142. Springer, Cham. https://doi.org/10.1007/978-3-030-50433-5_39